This invention relates generally to processing image data acquired using a positron emission tomography (PET) system, and more particularly, to segmenting lesions within PET image datasets.
There is no single generally accepted algorithm for segmenting lesions on PET images that works well under all conditions. Based on the type of the lesion, the location of the lesion in the patient's body, and the general PET image characteristics, such as noise, average standardized uptake values (SUV) levels, and a variety of artifacts which may be present in the image, the quality of the segmentation varies.
Typically, the user may need to manually select and fine-tune a segmentation algorithm from a plurality of algorithms to best fit the context of a particular image. This is time consuming, requires a high level of expertise, and the image may need to be reprocessed if the segmentation is not correct.
Also, the lesions of interest may be imaged over a period of time as a patient is followed during the course of tracking a pathology or treatment. It is desirable to compare the image datasets acquired over time to identify changes. However, the segmentation algorithm used in one or more prior images may not work correctly on a subsequent image dataset of the same anatomy. It is therefore necessary for the user to repeat the manual process of selecting and fine-tuning the segmentation algorithm to fit the context of the latest image.
Therefore, a need exists for automatically identifying a segmentation algorithm which bests fits the given context of a PET image dataset. Certain embodiments of the present invention are intended to meet these needs and other objectives that will become apparent from the description and drawings set forth below.